Senior Product Manager - Machine Learning

Yassir; India; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

Yassir is seeking a data-driven ML-focused product lead to guide discovery, problem framing and impact assessment through rigorous experimentation across its marketplace (ride-hailing, delivery and fintech). You will work with data scientists, ML engineers and product teams to ship production ML features and monitor model performance.

You will partner with cross-market teams, shape the ML roadmap, and ensure alignment with operations and risk considerations while driving measurable business

Qualifications

• 4+ years of product management experience including ML-driven products in production
• Hands-on fluency with data: write SQL and explore data independently
• Experience designing, running and interpreting controlled experiments and causal inference
• Strong understanding of core ML concepts: supervised learning, metrics, bias, calibration
• Track record shipping ML products with measurable business impact
• Experience working with distributed or remote teams across markets
• Background in marketplaces, mobility, on-demand delivery or fintech is a plus
• BSc/MSc in Engineering, Computer Science, Statistics, Data Science or related field

Responsibilities

• Data-led discovery: size ML opportunities from the data and translate into a backlog
• Problem framing: convert business problems into well-posed ML problems
• Metrics and success criteria: map model metrics to product/business metrics
• Experimentation and causal inference: design/run controlled experiments and interpret results
• ML product lifecycle: framing, data needs, baseline, iteration, rollout, monitoring
• Roadmap and trade-offs: balance accuracy, latency, cost, interpretability, regulatory needs
• Stakeholder alignment: communicate impact clearly to leadership and partners
• Leadership and culture: promote data-driven decision making and mentorship

Skills

ML product framing

SQL

Experimental design

Causal inference

Data-driven decision making

Cross-functional collaboration

Production ML lifecycle

Education

BSc/MSc in Engineering/CS/Statistics/Data Science

Tools

SQL

Job description

About Yassir: Yassir is the leading super app in the Maghreb region, set on changing the way daily services are provided. It currently operates in 45 cities across Algeria, Morocco and Tunisia, with recent expansions into France, Canada and Sub-Saharan Africa. It is backed (~$200M in funding) by VCs from Silicon Valley, Europe and other parts of the world. We offer on-demand services such as ride-hailing and last-mile delivery. Building on this infrastructure, we are now introducing financial services to help our users pay, save and borrow digitally. We're helping usher the continent into a digital economy era, not just by serving people, but by building a marketplace that brings people what they need while infusing social values.

About the Role

Yassir's marketplace runs on decisions made millions of times a day: which driver gets which trip, what a ride should cost, when an order will arrive, which transaction looks fraudulent, who qualifies for credit. This is a product role for ML-heavy, data-intensive products. You will lead discovery from data rather than from opinion, frame business problems as problems a model can actually solve, define what success means both offline and in production, and prove impact through well-designed experiments. You will work closely with data scientists, ML engineers and data engineers, and with product and operations partners across ride-hailing, delivery and financial services.

What This Role Is (and Isn't)

This role centres on predictive and decisioning ML: ranking, matching, forecasting, pricing, risk and personalisation. Experience building LLM or agentic features is welcome, but it is not a substitute for the fundamentals below. If your ML product experience is primarily integrating third-party models or APIs into user-facing features, this role is likely not the right fit.

Responsibilities

• Data-led discovery: Identify and size ML opportunities by going into the data yourself. Diagnose where the marketplace is losing value (supply-demand imbalance, cancellations, ETA error, fraud losses, default rates) and translate it into a prioritised, quantified problem backlog.
• Problem framing: Turn business problems into well-posed ML problems: define the prediction target, the decision it informs, the unit of analysis, label availability and quality, and the cost of different error types. Know when a problem does not need ML and a rule or heuristic will do.
• Metrics and success criteria: Define the chain from model metrics (e.g. precision/recall, calibration, MAE) to product and business metrics, and own guardrail metrics. Understand why offline gains often fail to translate online, and plan for it.
• Experimentation and causal inference: Design and interpret controlled experiments, including in two-sided marketplace settings where interference makes standard A/B tests unreliable (switchback, geo or cluster-randomised designs). Reason about statistical power, novelty effects and heterogeneous impact across cities and segments. Distinguish correlation from causation, and know which quasi-experimental methods to use when randomisation isn't possible.
• ML product lifecycle: Own models from framing through data requirements, baseline, iteration, launch, monitoring and retirement. Partner with engineering on rollout strategy, model monitoring, drift detection, retraining cadence and failure modes. Treat a model in production as a product that degrades if unattended.
• Roadmap and trade-offs: Own the ML product roadmap across domains and countries. Make explicit trade-offs between accuracy, latency, cost, interpretability, fairness and regulatory requirements, especially in credit and financial services.
• Stakeholder alignment and visibility: Make the impact of ML work legible to non-technical leadership. Communicate results, including null and negative results, with rigour and clarity. Align product, operations, risk and marketing partners on shared objectives.
• Leadership and culture: Raise the bar on how the organisation makes decisions with data. Mentor peers, build a culture of feedback and trust, and invest in your own growth and that of those around you.

Requirements

• 4+ years of product management experience, including at least 2 years owning ML-driven products that run in production and influence core business decisions (e.g. pricing, matching, ranking, forecasting, fraud, credit risk, recommendations).
• Hands-on fluency with data: you can write SQL, explore data independently and challenge an analysis without waiting for someone else to run it.
• Demonstrated experience designing, running and interpreting controlled experiments, and a solid working understanding of causal inference.
• Strong understanding of core ML concepts: supervised learning, evaluation metrics and their trade-offs, overfitting and leakage, bias, calibration, and the relationship between offline and online performance.
• A track record of shipping ML products where you can clearly articulate the problem framing, the metrics chosen, the experiment design and the measured business impact, including what went wrong.
• Experience working with distributed or remote teams across multiple markets.
• Experience in marketplaces, mobility, on-demand delivery or fintech is a strong plus.
• BSc/MSc in Engineering, Computer Science, Statistics, Data Science or a related quantitative field.